Technical Field
The present disclosure relates to systems, devices and methods for detecting and predicting neurological dysfunction characterized by abnormal electrographic patterns, and more particularly to neurological event detection tools for implantable medical devices, where the detection tools are characterized by programmable parameters that provide for refined detection and prediction of epileptic seizures and seizure onsets by analyzing electroencephalogram (EEG) and electrocorticogram (ECoG) signals.
Background
Epilepsy is a neurological disorder in which the nerve cell activity in the brain is disturbed, causing a seizure during which a person experiences abnormal behavior, symptoms and sensations, including for example, loss of consciousness, abnormal motor phenomena, psychic or sensory disturbances, or the perturbation of the autonomic nervous system. The episodic attacks or seizures experienced by a typical epilepsy patient are characterized by periods of abnormal neurological activity. As is traditional in the art, such periods shall be referred to herein as “ictal”. “Epileptiform” activity refers to specific neurological activity associated with epilepsy as well as with an epileptic seizure and its precursors. Such activity is frequently manifested in electrographic signals in the patient's brain.
Electrical stimulation may be used to treat epilepsy. Responsive stimulation involves detecting abnormal neurological activity (e.g., ictal and epileptiform activity), determining when the detected activity represents a neurological event, and then triggering delivery of electrical stimulation when an event is detected. Implantable neurostimulators are known that use algorithms of relatively low computational complexity to detect the activity of interest and to process the information to determine whether an event should be deemed detected in order to preserve the life of the implant power source (e.g., a primary cell battery). One such algorithm involves identifying half waves in sensed EEG signals that are conditioned and processed by the implanted medical device. A so-called “half wave detector” analyzes a signal in the time domain to estimate the power of the signal in various frequency bands. U.S. Pat. No. 6,810,285 to Pless et al. for “Seizure Sensing and Detection Using an Implantable Device” describes a half wave detector that can be used alone or in combination with other forms of data analysis to decide whether an event has occurred that merits triggering a form of electrical stimulation in response. U.S. Pat. No. 6,810,285 is incorporated herein in the entirety by reference. In general, the signals of interest represent aggregate neuronal activity potentials (local field potentials or LFPs) detectable via electrodes. When the electrodes are applied to a patient's scalp, the signals acquired are usually referred to as an EEG. When the electrodes are applied intracranially, such as placed on or near the surface of the brain (e.g., on or near the dura mater) or within the brain (e.g., via depth electrodes), the signals acquired may be referred to as an ECoG (electrocorticogram) or ECoGs (electrocorticographic signals) . . . . Unless the context clearly and expressly indicates otherwise, the term “EEG” shall be used generically herein to refer to both EEG and ECoG signals. Responsive stimulation involves the application of electrical stimulation in response to detection of epileptiform activity.
Other approaches to analyzing EEG signals involve transforming them into the frequency domain for rigorous spectrum analysis. See, e.g., U.S. Pat. No. 5,995,868 to Dorfineister et al., which analyzes the power spectral density of EEG signals in comparison to background characteristics. Although such approaches generally are believed to achieve good results, for the most part, they can be more computationally expensive than time domain analyses, making them less attractive for use in an implant that is intended to be implanted chronically. Whenever the analysis is being carried out in an implantable device, the real estate and power required to implement it is always an important design consideration. All of these approaches provide useful information, and in some cases may provide sufficient information for accurate detection and prediction of most imminent epileptic seizures. However, none of the various implementations of the known approaches provide 100% seizure detection accuracy in a clinical environment.
With any analysis, an objective is to avoid detection errors such as false positives and false negatives. A “false positive” refers to a detection of an ictal or epileptiform activity when no such abnormal activity is actually occurring. A “false negative” refers to the failure to detect abnormal activity when it is, in fact, occurring or, in some circumstances, when it is about to occur.
Detection tools or algorithms often can be tailored or tuned to detect activity that is abnormal for a particular patient: What is abnormal for one patient may be different than what is abnormal for another. Ideally, a detection algorithm would be tunable to capture all of the abnormal activity of interest and nothing that is not abnormal, that is, no false negatives and no false positives. A detection algorithm that results in no false positives and no false negatives may be described as having 100% specificity (no false positives) and 100% sensitivity (no false negatives). However, it is likely that when a detection algorithm is tuned to catch all of the abnormal activity (e.g., ictal and epileptiform), there will be a significant number of false positives. When the results of a detection algorithm determine when stimulation is delivered to the patient, it of course is desirable to minimize false positives, so that the patient is not being stimulated unless the abnormal activity of interest is occurring. Similarly, it is desirable not to miss any instances of the activity of interest and thus an objective with any detection algorithm is to avoid false negatives.
Thus, there is a need for an implantable device that can detect events in EEG activity that correlate to abnormal neurological activity in a more refined manner relative to existing techniques, without excessive computational complexity but nevertheless with a controllably low rate of false positives and/or false negatives.